Kershaw, Daniel and Rowe, Matthew and Stacey, Patrick (2016) Towards modelling language innovation acceptance in online social networks. In: WSDM '16 Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ACM, USA, pp. 553-562. ISBN 9781450337168
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Abstract
Language change and innovation is constant in online and offline communication, and has led to new words entering people’s lexicon and even entering modern day dictionaries, with recent additions of ‘e-cig’ and ‘vape’. However the manual work required to identify these ‘innovations’ is both time consuming and subjective. In this work we demonstrate how such innovations in language can be identified across two different OSN’s (Online Social Networks) through the operationalisation of known language acceptance models that incorporate relatively simplistic statistical tests. From grounding our work in language theory, we identified three statistical tests that can be applied, variation in; frequency, form and meaning; each showing different success rates across the two networks (Geo-bound Twitter sample and a sample of Reddit). These tests were also applied to different community levels within the two networks allow- ing for different innovations to be identified across different community structures over the two networks, for instance: identifying regional variation across Twitter, and variation across groupings of Subreddits, where identified example in- novations included ‘casualidad’ and ‘cym’.